The Tree of Decisions: Decision Trees from First Principles (Paperback) (ISBN-13: 9798190382495)

What if machine learning could begin with a question you already ask every day?
Should this request be handled now or later? Does this case need human review? Which detail changes the next step?
The Tree of Decisions turns those familiar moments into a gentle, visual introduction to one of the most understandable ideas in artificial intelligence: the decision tree.
Written for nontechnical readers, this book begins before the formulas. It starts with ordinary choices, clear conversations, and small hand-built examples. Step by step, you will see how a difficult problem becomes a sequence of questions; how examples become data; how roots, branches, and leaves create a visible path; and how a model learns to choose useful splits.
When the mathematics arrives, it has a purpose. Counts, proportions, Gini impurity, entropy, weighted averages, information gain, error, depth, validation, overfitting, and pruning are explained from first principles in plain language. No programming experience is required.
But a readable model is not automatically a trustworthy model. The book also examines biased labels, proxy variables, data leakage, unstable thresholds, distribution shift, uncertain cases, and the need for human oversight. Applications in customer support, education, healthcare support, finance, agriculture, manufacturing, and public services show both the value and the limits of tree-based reasoning.
Inside, readers will:

• build a complete decision tree by hand;
• follow new cases from root to leaf;
• compare candidate splits;
• understand why deeper is not always better;
• learn when to use, adapt, or avoid a decision tree; and
• practise with recall maps, chunking exercises, a capstone project, a glossary, and a thirty-day plan.

This is not a formula-first textbook or a coding manual. It is a fear-reducing bridge from everyday reasoning to machine learning for beginners, created to help curious readers understand how decisions become data, how data becomes a model, and how responsible judgment must remain visible.
If artificial intelligence has ever felt distant, mathematical, or difficult to question, this book offers a calmer starting point: one honest question, one branch, and one layer of understanding at a time.

Author: Ravindra Kumar Nayak
ISBN-13: 9798190382495
Publisher: Independently Published
Language: English
Published: 08/02/2026
Pages: 244
Format: Paperback
Weight: 0.66lbs
Size: 9.00h x 6.00w x 0.61d